A Biologically Inspired Associative Memory for Artificial Olfaction

Miquel Tarzan-Lorente 1 Agustin Gutierrez-Galvez 1 Dominique Martinez 2 Santiago Marco 1
2 CORTEX - Neuromimetic intelligence
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper, we propose a biologically inspired architecture for a Hopfield-like associative memory applied to artificial olfaction. The proposed algorithm captures the projection between two neural layers of the insect olfactory system (Antennal Lobe and Mushroom Body) with a kernel based projection. We have tested its classification performance as a function of the size of the training set and the time elapsed since training and compared it with that obtained with a Support Vector Machine.
Type de document :
Communication dans un congrès
International Joint Conference on Neural Networks - IJCNN 2010, 2010, Barcelone, Spain. IEEE, 2010
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https://hal.inria.fr/inria-00543032
Contributeur : Dominique Martinez <>
Soumis le : dimanche 5 décembre 2010 - 14:32:02
Dernière modification le : vendredi 30 mars 2018 - 14:57:01

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  • HAL Id : inria-00543032, version 1

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Miquel Tarzan-Lorente, Agustin Gutierrez-Galvez, Dominique Martinez, Santiago Marco. A Biologically Inspired Associative Memory for Artificial Olfaction. International Joint Conference on Neural Networks - IJCNN 2010, 2010, Barcelone, Spain. IEEE, 2010. 〈inria-00543032〉

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